Momentary predictors of dietary lapse from a mobile health weight loss intervention.
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| Title: | Momentary predictors of dietary lapse from a mobile health weight loss intervention. |
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| Authors: | Crochiere, Rebecca J., Abber, Sophie R., Taylor, Lauren C., Sala, Margaret, Schumacher, Leah M., Goldstein, Stephanie P., Forman, Evan M. |
| Source: | Journal of Behavioral Medicine. Apr2022, Vol. 45 Issue 2, p324-330. 7p. 1 Chart, 1 Graph. |
| Subjects: | Obesity risk factors, Affect (Psychology), Ingestion, Psychological tests, Preventive health services, Weight loss, Health behavior, Descriptive statistics, Forecasting, Health promotion, Telemedicine |
| Abstract: | Identifying factors that influence risk of dietary lapses (i.e., instances of dietary non-adherence) is important because lapses contribute to suboptimal weight loss outcomes. Existing research examining lapse risk factors has had methodological limitations, including retrospective recall biases, subjective operationalizations of lapse, and has investigated lapses among participants in gold-standard behavioral weight loss programs (which are not accessible to most Americans). The current study will address these limitations by being the first to prospectively assess several risk factors of lapse (objectively operationalized) in the context of a commercial mobile health (mHealth) intervention, a highly popular and accessible method of weight loss. N = 159 adults with overweight or obesity enrolled in an mHealth commercial weight loss program completed ecological momentary assessments (EMAs) of 15 risk factors and lapses (defined as exceeding a point target for a meal/snack) over a 2-week period. N = 9 participants were excluded due to low EMA compliance, resulting in a sample of N = 150. Dietary lapses were predicted by momentary increases in urges to deviate from one's eating plan (b =.55, p <.001), cravings (b =.55, p <.001), alcohol consumption (b =.51, p <.001), and tiredness (b =.19, p <.001), and decreases in confidence related to meeting dietary goals (b = −.21, p <.001) and planning food intake (b = −.15, p <.001). This study was among the first to identify prospective predictors of lapse in the context of a commercial mHealth weight loss program. Findings can inform mHealth weight loss programs, including just-in-time interventions that measure these risk factors, calculate when risk of lapse is high, and deliver momentary interventions to prevent lapses. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Identifying factors that influence risk of dietary lapses (i.e., instances of dietary non-adherence) is important because lapses contribute to suboptimal weight loss outcomes. Existing research examining lapse risk factors has had methodological limitations, including retrospective recall biases, subjective operationalizations of lapse, and has investigated lapses among participants in gold-standard behavioral weight loss programs (which are not accessible to most Americans). The current study will address these limitations by being the first to prospectively assess several risk factors of lapse (objectively operationalized) in the context of a commercial mobile health (mHealth) intervention, a highly popular and accessible method of weight loss. N = 159 adults with overweight or obesity enrolled in an mHealth commercial weight loss program completed ecological momentary assessments (EMAs) of 15 risk factors and lapses (defined as exceeding a point target for a meal/snack) over a 2-week period. N = 9 participants were excluded due to low EMA compliance, resulting in a sample of N = 150. Dietary lapses were predicted by momentary increases in urges to deviate from one's eating plan (b =.55, p <.001), cravings (b =.55, p <.001), alcohol consumption (b =.51, p <.001), and tiredness (b =.19, p <.001), and decreases in confidence related to meeting dietary goals (b = −.21, p <.001) and planning food intake (b = −.15, p <.001). This study was among the first to identify prospective predictors of lapse in the context of a commercial mHealth weight loss program. Findings can inform mHealth weight loss programs, including just-in-time interventions that measure these risk factors, calculate when risk of lapse is high, and deliver momentary interventions to prevent lapses. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 01607715 |
| DOI: | 10.1007/s10865-021-00264-4 |
